Enhancing Demand-Oriented Regionalization with Agentic AI and Local Heterogeneous Data for Adaptation Planning

TL;DR

This paper presents an agentic AI-powered platform that creates demand-oriented regional units for disaster planning, integrating human input and local data to improve hazard response strategies.

Contribution

It introduces a novel spatially constrained self-organizing map with AI agents for dynamic, demand-driven regionalization supporting hazard prevention planning.

Findings

Effective regionalization for flood risk in Jacksonville

Enhanced user interaction and decision support

Integration of AI reasoning with spatial data

Abstract

Conventional planning units or urban regions, such as census tracts, zip codes, or neighborhoods, often do not capture the specific demands of local communities and lack the flexibility to implement effective strategies for hazard prevention or response. To support the creation of dynamic planning units, we introduce a planning support system with agentic AI that enables users to generate demand-oriented regions for disaster planning, integrating the human-in-the-loop principle for transparency and adaptability. The platform is built on a representative initialized spatially constrained self-organizing map (RepSC-SOM), extending traditional SOM with adaptive geographic filtering and region-growing refinement, while AI agents can reason, plan, and act to guide the process by suggesting input features, guiding spatial constraints, and supporting interactive exploration. We demonstrate the capabilities of the platform through a case study on the flooding-related risk in Jacksonville, Florida, showing how it allows users to explore, generate, and evaluate regionalization interactively, combining computational rigor with user-driven decision making.